Prediction of Galloping Accidents in Power Transmission Line Using Logistic Regression Analysis

Abstract

-Galloping is one of the most serious vibration problems in transmission lines. Power lines can be extensively damaged owing to aerodynamic instabilities caused by ice accretion. In this study, the accident probability induced by galloping phenomenon was analyzed using logistic regression analysis. As former studies have generally concluded, main factors considered were local weather factors and physical factors of power delivery systems. Since the number of transmission towers outnumbers the number of weather observatories, interpolation of weather factors, Kriging to be more specific, has been conducted in prior to forming galloping accident estimation model. Physical factors have been provided by Korea Electric Power Corporation, however because of the large number of explanatory variables, variable selection has been conducted, leaving total 11 variables. Before forming estimation model, with 84 provided galloping cases, 840 non-galloped cases were chosen out of 13 billion cases. Prediction model for accidents by galloping has been formed with logistic regression model and validated with 4-fold validation method, corresponding AUC value of ROC curve has been used to assess the discrimination level of estimation models. As the result, logistic regression analysis effectively discriminated the power lines that experienced galloping accidents from those that did not.

Transmission Line Galloping Prediction Based on GA-BP-SVM Combined Method

“…In the literature [14], a back-propagation (BP) neural network model is proposed to predict the galloping features including the frequencies, vibration amplitudes and the maximum conductor tension using span length, initial wind angle of attack, and wind speed as input variables. In the literature [15], the accident probability induced by galloping is analyzed using logistic regression analysis. The discriminant level of the computed model is evaluated using the AUC values of the ROC curves of the prediction model.…”